Application Lifecycle Management on Microsoft Power Platform. A comprehensive guide to managing the deployment of your solutions

Benedikt Bergmann, Scott Durow

Managing Power Platform solutions manually can be challenging and time-consuming, as is application lifecycle management (ALM), which encompasses governance, development, and maintenance. This book provides comprehensive coverage of ALM, addressing planning, development, testing, deployment, and maintenance. Drawing on his extensive experience as a Power Platform consultant and Microsoft MVP, Benedikt Bergmann simplifies complex topics, making them accessible and easy to grasp.From planning and designing applications to deploying and maintaining them, this book provides step-by-step instructions, best practices, and real-world examples to effectively manage the entire application lifecycle. You’ll gain insights into optimizing Power Platform's toolbox, including Power Apps, Power Automate, Power Pages, and Power Virtual Agents, for seamless collaboration, agile development, and rapid application delivery. You’ll also implement best practices for version control, code management, and collaboration using the Microsoft Power Platform.By the end of this book, you’ll be equipped with the knowledge and skills to effectively manage the entire application lifecycle, accelerate development cycles, and deliver exceptional solutions with the Microsoft Power Platform.

Entity Framework Core Cookbook. Transactions, stored procedures, query libraries, and more - Second Edition

Ricardo Peres

Entity Framework is a highly recommended Object Relation Mapping tool used to build complex systems. In order to survive in this growing market, the knowledge of a framework that helps provide easy access to databases, that is, Entity Framework has become a necessity. This book will provide .NET developers with this knowledge and guide them through working efficiently with data using Entity Framework Core. You will start off by learning how to efficiently use Entity Framework in practical situations. You will gain a deep understanding of mapping properties and find out how to handle validation in Entity Framework. The book will then explain how to work with transactions and stored procedures along with improving Entity Framework using query libraries. Moving on, you will learn to improve complex query scenarios and implement transaction and concurrency control. You will then be taught to improve and develop Entity Framework in complex business scenarios. With the concluding chapter on performance and scalability, this book will get you ready to use Entity Framework proficiently.

50 Algorithms Every Programmer Should Know. Tackle computer science challenges with classic to modern algorithms in machine learning, software design, data systems, and cryptography - Second Edition

Imran Ahmad, Somaieh Nikpoor

The ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works.You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them.Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use.You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT.Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.

Open Text Metastorm ProVision 6.2 Strategy Implementation. Create and implement a successful business strategy for improved performance throughout the whole enterprise

Bill Aronson

Open Text ProVision® (formerly known as Metastorm ProVision®) is an Enterprise Architecture (EA) solution allowing for effective planning and decision making throughout the enterprise. It enables an organization to have a central repository of information about the business, reducing organizational risks and better optimizing business resources.Implemented well, it enables better and more actionable decisions exactly when you need them.This book combines theory and practice to provide a step- by- step guide to building a successful customer- centric model of your business. The approach is simple and down to earth, and along the way, with various real-world examples, you will learn how to make a business case, use a framework, and adopt a methodology with Open Text ProVision®. This book draws on the experience of ProVision® experts around the world. By combining theory with practice from the field you can avoid common mistakes and develop a successful customer centric strategy for implementing ProVision®. Each chapter builds on the previous one to give you the confidence to implement a central repository, dealing with both the technical and human issues that you might face.

Business Intelligence with Databricks SQL. Concepts, tools, and techniques for scaling business intelligence on the data lakehouse

Vihag Gupta

In this new era of data platform system design, data lakes and data warehouses are giving way to the lakehouse – a new type of data platform system that aims to unify all data analytics into a single platform. Databricks, with its Databricks SQL product suite, is the hottest lakehouse platform out there, harnessing the power of Apache Spark™, Delta Lake, and other innovations to enable data warehousing capabilities on the lakehouse with data lake economics.This book is a comprehensive hands-on guide that helps you explore all the advanced features, use cases, and technology components of Databricks SQL. You’ll start with the lakehouse architecture fundamentals and understand how Databricks SQL fits into it. The book then shows you how to use the platform, from exploring data, executing queries, building reports, and using dashboards through to learning the administrative aspects of the lakehouse – data security, governance, and management of the computational power of the lakehouse. You’ll also delve into the core technology enablers of Databricks SQL – Delta Lake and Photon. Finally, you’ll get hands-on with advanced SQL commands for ingesting data and maintaining the lakehouse.By the end of this book, you’ll have mastered Databricks SQL and be able to deploy and deliver fast, scalable business intelligence on the lakehouse.

The Supervised Learning Workshop. Predict outcomes from data by building your own powerful predictive models with machine learning in Python - Second Edition

Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur

Would you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society.Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models.Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks.By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.

React and React Native. A complete hands-on guide to modern web and mobile development with React.js - Third Edition

Adam Boduch, Roy Derks

React and React Native, Facebook’s innovative User Interface (UI) libraries, are designed to help you build robust cross-platform web and mobile applications. This updated third edition is improved and updated to cover the latest version of React. The book particularly focuses on the latest developments in the React ecosystem, such as modern Hook implementations, code splitting using lazy components and Suspense, user interface framework components using Material-UI, and Apollo. In terms of React Native, the book has been updated to version 0.62 and demonstrates how to apply native UI components for your existing mobile apps using NativeBase.You will begin by learning about the essential building blocks of React components. Next, you’ll progress to working with higher-level functionalities in application development, before putting this knowledge to use by developing user interface components for the web and for native platforms. In the concluding chapters, you’ll learn how to bring your application together with a robust data architecture.By the end of this book, you’ll be able to build React applications for the web and React Native applications for multiple mobile platforms.

Practical Machine Learning with R. Define, build, and evaluate machine learning models for real-world applications

Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu

With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way.Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it.